Eric T. Nalisnick

According to our database1, Eric T. Nalisnick authored at least 46 papers between 2013 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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In proceedings 
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PhD thesis 
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Links

On csauthors.net:

Bibliography

2024
Crowd-Calibrator: Can Annotator Disagreement Inform Calibration in Subjective Tasks?
CoRR, 2024

Test-Time Adaptation with State-Space Models.
CoRR, 2024

Fast yet Safe: Early-Exiting with Risk Control.
CoRR, 2024

Uncertainty Aware Tropical Cyclone Wind Speed Estimation from Satellite Data.
CoRR, 2024

Adaptive Bounding Box Uncertainties via Two-Step Conformal Prediction.
CoRR, 2024

A Generative Model of Symmetry Transformations.
CoRR, 2024

On the Challenges and Opportunities in Generative AI.
CoRR, 2024

Learning to Defer to a Population: A Meta-Learning Approach.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023
Beyond Top-Class Agreement: Using Divergences to Forecast Performance under Distribution Shift.
CoRR, 2023

One Strike, You're Out: Detecting Markush Structures in Low Signal-to-Noise Ratio Images.
CoRR, 2023

Anytime-Valid Confidence Sequences for Consistent Uncertainty Estimation in Early-Exit Neural Networks.
CoRR, 2023

Active Learning for Multilingual Fingerspelling Corpora.
CoRR, 2023

Exploiting Inferential Structure in Neural Processes.
Proceedings of the Uncertainty in Artificial Intelligence, 2023

Towards Anytime Classification in Early-Exit Architectures by Enforcing Conditional Monotonicity.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Sampling-based inference for large linear models, with application to linearised Laplace.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Learning to Defer to Multiple Experts: Consistent Surrogate Losses, Confidence Calibration, and Conformal Ensembles.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

Do Bayesian Neural Networks Need To Be Fully Stochastic?
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022
Hate Speech Criteria: A Modular Approach to Task-Specific Hate Speech Definitions.
CoRR, 2022

Adversarial Defense via Image Denoising with Chaotic Encryption.
CoRR, 2022

Calibrated Learning to Defer with One-vs-All Classifiers.
Proceedings of the International Conference on Machine Learning, 2022

Adapting the Linearised Laplace Model Evidence for Modern Deep Learning.
Proceedings of the International Conference on Machine Learning, 2022

On the impact of non-IID data on the performance and fairness of differentially private federated learning.
Proceedings of the 52nd Annual IEEE/IFIP International Conference on Dependable Systems and Networks, 2022

2021
Normalizing Flows for Probabilistic Modeling and Inference.
J. Mach. Learn. Res., 2021

Bayesian Deep Learning via Subnetwork Inference.
Proceedings of the 38th International Conference on Machine Learning, 2021

How Emotionally Stable is ALBERT? Testing Robustness with Stochastic Weight Averaging on a Sentiment Analysis Task.
Proceedings of the 2nd Workshop on Evaluation and Comparison of NLP Systems, 2021

Predictive Complexity Priors.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020
Expressive yet Tractable Bayesian Deep Learning via Subnetwork Inference.
CoRR, 2020

2019
Detecting Out-of-Distribution Inputs to Deep Generative Models Using a Test for Typicality.
CoRR, 2019

Bayesian Batch Active Learning as Sparse Subset Approximation.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Hybrid Models with Deep and Invertible Features.
Proceedings of the 36th International Conference on Machine Learning, 2019

Dropout as a Structured Shrinkage Prior.
Proceedings of the 36th International Conference on Machine Learning, 2019

Do Deep Generative Models Know What They Don't Know?
Proceedings of the 7th International Conference on Learning Representations, 2019

2018
On Priors for Bayesian Neural Networks.
PhD thesis, 2018

Unifying the Dropout Family Through Structured Shrinkage Priors.
CoRR, 2018

Bayesian Trees for Automated Cytometry Data Analysis.
Proceedings of the Machine Learning for Healthcare Conference, 2018

The Effectiveness of a two-Layer Neural Network for Recommendations.
Proceedings of the 6th International Conference on Learning Representations, 2018

Learning Priors for Invariance.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017
Learning Approximately Objective Priors.
Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence, 2017

Variational Reference Priors.
Proceedings of the 5th International Conference on Learning Representations, 2017

Stick-Breaking Variational Autoencoders.
Proceedings of the 5th International Conference on Learning Representations, 2017

2016
A Dual Embedding Space Model for Document Ranking.
CoRR, 2016

Improving Document Ranking with Dual Word Embeddings.
Proceedings of the 25th International Conference on World Wide Web, 2016

Analyzing NIH Funding Patterns over Time with Statistical Text Analysis.
Proceedings of the Scholarly Big Data: AI Perspectives, 2016

2015
Infinite Dimensional Word Embeddings.
CoRR, 2015

2013
Extracting Sentiment Networks from Shakespeare's Plays.
Proceedings of the 12th International Conference on Document Analysis and Recognition, 2013

Character-to-Character Sentiment Analysis in Shakespeare's Plays.
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, 2013


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